class_docstr: Computes Precision, Recall and F1 with respect to a given `positive_label`.
  For example, for a BIO tagging scheme, you would pass the classification index of
  the tag you are interested in, resulting in the Precision, Recall and F1 score being
  calculated for this tag only.
code: def __init__(self, positive_label: int) -> None:
      super().__init__(beta=1, labels=[positive_label])
      self._positive_label = positive_label
  
file_path: allennlp/allennlp/training/metrics/f1_measure.py
imports: from typing import Dict
  from allennlp.training.metrics.metric import Metric
  from allennlp.training.metrics.fbeta_measure import FBetaMeasure
label_desc: Use image node_id 1 to create a new F1Measure object from inherited base classes: FBetaMeasure with example: obj = F1Measure(positive_label)